1. Field of the Invention
The present invention relates to generally to encoding and decoding of digital data using differential pulse code modulation (DPCM) techniques. More particularly, the invention relates to a subset averaged median (SAM) predictor for a DPCM system, which isolates errors and minimizes error propagation when coding information such as image and voice data.
2. Description of the Related Art
FIG. 1 is a block diagram illustrating a prior art differential pulse code modulation (DPCM) system. The DPCM system includes an encoder 10 having a quantizer 11 and a linear or median-type predictor 12, as well as a decoder 20 with a linear or median-type predictor 12. X(n) is an original input signal, and E(n) is an actual error signal. On the encoding side, predictor 12 predicts the next signal sample to generate a predicted output Xxe2x80x2(n) based on the values of immediately preceding signal samples. A subtractor 15 subtracts Xxe2x80x2(n) from X(n) to generate an error signal which is quantized by quantizer 11 to generate the error signal E(n). Xxe2x80x2(n) is also fed back and summed with the error signal by summer 17 to provide the next sample to predictor 12. The error signal E(n) is transmitted in a transmission channel and is received by decoder 20 as prediction error signal Er(n). This signal is added to a predicted signal Xrxe2x80x2(n) by adder 19 to generate a reconstructed signal Xr(n) based on Er(n).
Equations governing the respective operation of linear and median-type predictors are as follows:
xe2x80x83Linear predictor: LIN(X)=0.5X1+0.25(X2+X3)xe2x80x83xe2x80x83(1)
First median-type predictor: MED1(X)=med(X1,X2,X3,X4)xe2x80x83xe2x80x83(2)
Second median-type predictor: MED2(X)=med(X1,X3,X4)xe2x80x83xe2x80x83(3)
Finite impulse response hybrid median-type predictor (FMH):
FMH(X)=med(X1,X2,X4,l,r)xe2x80x83xe2x80x83(4)
where l=X1+X3xe2x88x92X2, and r=med(X1,X2,X3,X4).
Input values used for prediction associated with a DPCM system are mapped as follows:
{X1,X2,X3,X4}={X(m,nxe2x88x921),X(mxe2x88x921,nxe2x88x921),X(mxe2x88x921,n),X(mxe2x88x921,n+1)}xe2x80x83xe2x80x83(5)
FIG. 2 shows coordinates of a two dimensional mapping input. Most predictors used for prediction of the X (m, n) value use only data which have a strong correlation with X(m, n) because the further the data are from X(m, n), the less contribution the data will have for compression of the signal.
The DPCM system of FIG. 1 traditionally employed a linear predictor for coding data such as image and voice data. Recently, a median-type predictor having a non-linear feature has been used in the DPCM system as an alternative.
Other examples of DPCM systems can be found in U.S. Pat. Nos. 4,430,670: 5,379,355 and 5,696,791.
Techniques to minimize prediction error variance are related to techniques for maintaining signal quality when reconstructing a compressed signal. A DPCM system employing a non-linear median-type predictor can prevent propagation of a reconstructed signal having a transmission error at a receiver, so that the transmission error can be isolated (i.e., removed).
When the receiver of the encoded data reconstructs the original signal by using the same predictor as used on the transmitting side, if a transmission error is generated, the Er(n) value is not identical to the E(n) value, whereby the original signal can not be accurately reconstructed. Since the DPCM system is a recursive system, when Er(n) differs from E(n) a reconstruction error of the nth sample affects the next sample to be reconstructed. If the reconstruction error affects the next signal to be reconstructed in such a manner, the error is essentially propagated. This phenomenon is called an error propagation effect.
Some systems adopt a type of predictor which maintains stability or periodically resets transmit/receive signals in order to diminish the propagation effect generated by transmission noise on a reconstructed signal. A DPCM system adapting a linear predictor, however, can not isolate transmission noise generated by a transmission error in a transmission line; thus, it simply strives to improve image or voice quality by minimizing prediction error variance.
If a linear filter (predictor) is employed, then theoretically a technique for checking and verifying the linear filter stability can be implemented. An advantage of such a technique is that the design of filter is simple. The filter is stable if a pole of a transfer function is in a unit circle; however, a shortcoming of this approach is that transmission noise can not be isolated.
Techniques for minimizing prediction error variance and decaying error propagation generated by transmission noise have been proposed. Some of these techniques allow transmission noise to be propagated only up until a certain transmission line section, stopping propagation of the noise after the section. These techniques exhibit shortcomings in that error propagation becomes severe without precise synchronization between a transmitter and a receiver.
A DPCM system employing a median-type predictor is designed to isolate transmission noise or to minimize a prediction error variance regardless of the input signal features. However, such an approach can not perfectly isolate the transmission noise, and the non-isolated transmission noise causes error propagation in a reconstructed signal at the receiver or causes interference in the reconstruction of an original signal. Also, since input signal features are not generally maintained the same, the median-type predictor lacks stability in that the predictor performance may change depending upon the input signals.
Consequently, a DPCM system incorporating a linear predictor can not isolate transmission noise due to a transmission error generated in a transmission line. A DPCM system incorporating a prior art median-type predictor that can isolate transmission noise can not be designed to minimize a prediction error variance in accordance with input signal features. Further, when the transmission noise is not isolated, the error may be propagated to a reconstructed signal at a receiver and interfere in reconstruction of an original signal.
The present invention is designed to overcome the foregoing problems of prior art DPCM systems.
It is an object of the present invention to provide a new type of predictor for differential pulse code modulation, designated herein as a subset averaged median (SAM) predictor, which can minimize prediction error variance in accordance with input signal features and at the same time either isolate transmission noise or induce a condition where error propagation is decayed.
In one aspect of the present invention, there is provided a SAM predictor for a DPCM system, which removes transmission errors and/or minimizes error propagation. The SAM predictor in a preferred embodiment operates according to             SAM      ⁢              (        X        )              =                  ∑                  i          =          1                P            ⁢              xe2x80x83            ⁢                        a          i                ⁢                              F            i                    ⁢                      (            X            )                                ,
where X is an input vector within a predictor window, P is the number of median subfilters, ai is an optionally selected coefficient and Fi( ) is a feature equation of an ith median subfilter. The coefficients as may be selected so as to minimize a prediction error variance and to exclude first order subfilters, where                     ∑                  i          =          1                P            ⁢              xe2x80x83            ⁢              "LeftBracketingBar"                  a          i                "RightBracketingBar"               less than     1    ,
thereby removing transmission error and minimizing prediction error variance through minimization of error propagation.
Other objects and advantages of the present invention will become apparent with reference to the following detailed description and the attached drawings.